How to reduce hallucination in a production LLM application?
This tests your ability to design a robust, multi-layered system for AI safety, not just your model knowledge. A great answer starts with data-level grounding (RAG), moves to model-level tuning (temperature, fine-tuning), and finishes with application-level safeguards (validation, feedback loops). A red flag is focusing only on prompt engineering or stating it's an unsolvable problem without offering concrete mitigation strategies.
This question assesses your practical, systems-level approach to building reliable AI products, moving beyond theoretical model knowledge. Interviewers want to see a multi-layered defense against false information, demonstrating an understanding of the entire application stack. A strong answer outlines a defense-in-depth strategy: first, data-level interventions like Retrieval-Augmented Generation (RAG) to ground the model; second, model-level techniques like fine-tuning and adjusting decoding parameters (temperature); and finally, application-level safeguards like output validation, structured outputs with citations, and user feedback loops. The most common red flag is focusing solely on prompt engineering or giving a fatalistic answer that it's an unsolved problem.
Read the original → Wikipedia: Hallucination (artificial intelligence)
- #llm
- #generative ai
- #system design
- #ai safety
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